Project Summary Alzheimer's disease (AD) pathology is characterized by the presence of phosphorylated tau in neurofibrillary tangles (NFTs), dystrophic neurites and abundant extracellular ?-amyloid in senile plaques. However, the etiology of AD remains elusive, partly due to the wide spectrum of clinical and neurobiological/neuropathological features in AD patients. Thus, heterogeneity in AD has complicated the task of discovering disease-modifying treatments and developing accurate in vivo indices for diagnosis and clinical prognosis. Different approaches have been proposed for AD subtyping, but they are generally neither suitable for high-dimensional data nor actionable due to the lack of mechanistic insights. Increased knowledge and understanding of different AD subtypes would shed light on recently failed clinical trials and provide for the potential to tailor treatments with specificity to more homogeneous subgroups of patients. By integrating genetic, molecular and neuroimaging data to more precisely define AD subtypes, we may be able to better discriminate between highly overlapping clinical phenotypes. Furthermore, the identification of such subtypes may potentially improve our understanding of its underlying pathomechanisms, prediction of its course, and the development of novel disease-modifying treatments. In this application, we propose to systematically identify and characterize molecular subtypes of AD by developing and employing cutting-edge network biology approaches to multiple existing large-scale genetic, gene expression, proteomic and functional MRI datasets. We will investigate the functional roles of key drivers underlying predicted AD subtypes as well as three candidate key drivers from our current AMP-AD consortia work in control and AD hiPSC-derived neural co-culture systems and then in complex organoids by screening the predicted transcriptional impact of top key drivers in single cell and cell-population-wide analyses. Functional assays in each cell type will be used to build evidence for relevance to AD-subtype phenotypes. Single cell RNA sequencing data will be generated to identify perturbation signatures in selected drivers that will then be mapped to subtype specific networks to build comprehensive signaling maps for each driver. The top three most promising drivers of AD subtypes and the three existing AMP-AD targets will be further validated using a) an independent postmortem cohort, and b) recombinant mice, including amyloidosis, tauopathy and new ?humanized? models.